Prince, E. W. | Search bioRxiv

bioRxiv2025

Differential Phagocytosis induces Diverse Macrophage Activation States in Malignant Gliomas

Diffuse midline glioma (DMG) and Glioblastoma are malignant brain tumors in pediatric and adult patients. The current standard-of-care treatment for DMG is radiotherapy (RT), whereas GBM treatment includes surgery, followed by RT and chemotherapy. Although RT is known to modulate immune responses in cancer and enhance the effectiveness of myeloid checkpoint blockade, the downstream macrophage responses to differential phagocytosis induction remain poorly understood. This study examined macrophage-mediated phagocytosis caused by either RT, anti-CD47 checkpoint blockade, or their combination. We found that RT increased the expression of several damage-associated molecular patterns on the surface of glioma cell lines. Furthermore, RT enhanced anti-CD47-mediated macrophage phagocytosis of glioma cell lines in vitro. Single-cell RNA-sequencing revealed the diverse transcriptional and functional signatures of human macrophage subsets that either promoted or inhibited phagocytosis of glioma cells pretreated with RT, anti-CD47 therapy, or both. Consistent with these results, the combination therapy significantly reduced tumor growth, prolonged survival in glioma-bearing mice, and induced distinct macrophage activation states in vivo compared to either treatment alone. These findings highlight the plasticity and heterogeneity of macrophage responses during phagocytosis and provide compelling evidence for combining RT with anti-CD47 therapy as a promising therapeutic strategy for glioma treatment.

cancer biology↗

bioRxiv2019

Linnaeus: Interpretable Deep Learning Classification of Single Cell Transcript Data

High throughput data is commonplace in biomedical research as seen with technologies such as single-cell RNA sequencing (scRNA-seq) and other Next Generation Sequencing technologies. As these techniques continue to be increasingly utilized it is critical to have analysis tools that can identify meaningful complex relationships between variables (i.e., in the case of scRNA-seq: genes) in a way such that human bias is absent. Moreover, it is equally paramount that both linear and non-linear (i.e., one-to-many) variable relationships be considered when contrasting datasets. HD Spot is a deep learning-based framework that generates an optimal interpretable classifier a given high-throughput dataset using a simple genetic algorithm as well as an autoencoder to classifier transfer learning approach. Using four unique publicly available scRNA-seq datasets with published ground truth, we demonstrate the robustness of HD Spot and the ability to identify ontologically accurate gene lists for a given data subset. HD Spot serves as a bioinformatic tool to allow novice and advanced analysts to gain complex insight into their respective datasets enabling novel hypotheses development.

bioinformatics↗